library(tidyverse)
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## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
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library(lubridate)
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
time_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
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## ── Column specification ─────────────────────────────────────────
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
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# Let's get the times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
##
## ── Column specification ─────────────────────────────────────────
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
# Create Keys
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
# Plot graph to a pdf outputfile
pdf("images/time_series_example_plot.pdf", width=6, height=3)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()
# Plot graph to a png outputfile
ppi <- 300
png("images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()

# This is an alternative way using html.
# Remember that it must be in your working directory or you will need to specify the full path.
# The html is put OUTSIDE the r code chunk.
<img src="images/time_series_example_plot.png" alt="US COVID-19 Deaths" style="width: 600px;"/>
# Version 2
library(plotly)
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## Attaching package: 'plotly'
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## last_plot
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## layout
ggplotly(
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
)
library(plotly)
# Subset the time series data to include US deaths
US_deaths <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US")
# Collect the layers for agraph of the US time series data for covid deaths
p <- ggplot(data = US_deaths, aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
# Plot the graph using ggplotly
ggplotly(p)
library(gganimate)
library(transformr)
library(gifski)
library(av)
theme_set(theme_bw())
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
animate(p, end_pause = 15)
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
anim_save("deaths_5_countries.gif", p)
#Exercise 1
ppi <- 300
png("Rplot01.png", width=3*ppi, height=3*ppi, res=ppi)
Top 10 Countries COVID-19 Deaths
#Exercise 2
library(plotly)
# Subset the time series data to include US deaths
US_confirmed <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US")
p <- ggplot(data = US_confirmed, aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Confirmed Cases")
# Plot the graph using ggplotly
ggplotly(p)
#Exercise 3
data_time_2 <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("United Kingdom","France","Italy","Brazil", "India","Peru", "Spain", "Iran", "Mexico", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) + geom_point() +
geom_line() +
ggtitle("COVID-19 Top 10 Death Totals") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
animate(data_time_2, end_pause = 15)
anim_save("deaths_10_countries.gif", data_time_2)